Overview

Dataset statistics

Number of variables11
Number of observations13213641
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 GiB
Average record size in memory88.0 B

Variable types

Numeric11

Alerts

LongitudAcc is highly correlated with Fuel Rate and 2 other fieldsHigh correlation
EngineSpeed is highly correlated with EngineAirInletPressure and 1 other fieldsHigh correlation
Fuel Rate is highly correlated with Engine Load and 1 other fieldsHigh correlation
Engine Load is highly correlated with Boost Pressure and 2 other fieldsHigh correlation
Boost Pressure is highly correlated with Engine Load and 2 other fieldsHigh correlation
EngineAirInletPressure is highly correlated with EngineSpeed and 3 other fieldsHigh correlation
AcceleratorPedalPos is highly correlated with Engine Load and 3 other fieldsHigh correlation
VehicleSpeed is highly correlated with EngineSpeedHigh correlation
BrakePedalPos is highly correlated with AcceleratorPedalPosHigh correlation
Fuel Rate is highly skewed (γ1 = 46.75265747) Skewed
Timestamp has unique values Unique
LongitudAcc has 3062406 (23.2%) zeros Zeros
EngineSpeed has 226548 (1.7%) zeros Zeros
Fuel Rate has 3065605 (23.2%) zeros Zeros
Engine Load has 3079422 (23.3%) zeros Zeros
Boost Pressure has 550952 (4.2%) zeros Zeros
AcceleratorPedalPos has 5299357 (40.1%) zeros Zeros
VehicleSpeed has 1889729 (14.3%) zeros Zeros
BrakePedalPos has 10745857 (81.3%) zeros Zeros

Reproduction

Analysis started2022-11-23 15:22:07.425704
Analysis finished2022-11-23 15:37:08.037861
Duration15 minutes and 0.61 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

Timestamp
Real number (ℝ≥0)

UNIQUE

Distinct13213641
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.821576415 × 1010
Minimum1.717422031 × 1010
Maximum8.030538652 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size100.8 MiB
2022-11-23T16:37:08.183656image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum1.717422031 × 1010
5-th percentile1.927590015 × 1010
Q12.801417485 × 1010
median5.497760062 × 1010
Q36.535583896 × 1010
95-th percentile7.74541506 × 1010
Maximum8.030538652 × 1010
Range6.313116622 × 1010
Interquartile range (IQR)3.734166411 × 1010

Descriptive statistics

Standard deviation2.025682083 × 1010
Coefficient of variation (CV)0.4201285862
Kurtosis-1.530225936
Mean4.821576415 × 1010
Median Absolute Deviation (MAD)1.971862689 × 1010
Skewness-0.04471569004
Sum6.37105798 × 1017
Variance4.1033879 × 1020
MonotonicityStrictly increasing
2022-11-23T16:37:08.335677image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.717422031 × 10101
 
< 0.1%
6.164821529 × 10101
 
< 0.1%
6.164820448 × 10101
 
< 0.1%
6.164820559 × 10101
 
< 0.1%
6.164820738 × 10101
 
< 0.1%
6.164820848 × 10101
 
< 0.1%
6.164820928 × 10101
 
< 0.1%
6.164821044 × 10101
 
< 0.1%
6.164821157 × 10101
 
< 0.1%
6.164821229 × 10101
 
< 0.1%
Other values (13213631)13213631
> 99.9%
ValueCountFrequency (%)
1.717422031 × 10101
< 0.1%
1.717422136 × 10101
< 0.1%
1.717422332 × 10101
< 0.1%
1.71742241 × 10101
< 0.1%
1.717422526 × 10101
< 0.1%
1.717422632 × 10101
< 0.1%
1.717422828 × 10101
< 0.1%
1.717422932 × 10101
< 0.1%
1.717423034 × 10101
< 0.1%
1.717423121 × 10101
< 0.1%
ValueCountFrequency (%)
8.030538652 × 10101
< 0.1%
8.030538545 × 10101
< 0.1%
8.030538460 × 10101
< 0.1%
8.030538353 × 10101
< 0.1%
8.030538253 × 10101
< 0.1%
8.030538161 × 10101
< 0.1%
8.030538053 × 10101
< 0.1%
8.030537945 × 10101
< 0.1%
8.030537847 × 10101
< 0.1%
8.030537758 × 10101
< 0.1%

WetTankAirPressure
Real number (ℝ≥0)

Distinct218
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.04196288
Minimum0
Maximum14.96215
Zeros270
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size100.8 MiB
2022-11-23T16:37:08.488098image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9.9288
Q110.68725
median11.23885
Q311.5836
95-th percentile11.9973
Maximum14.96215
Range14.96215
Interquartile range (IQR)0.89635

Descriptive statistics

Standard deviation1.06515459
Coefficient of variation (CV)0.09646424297
Kurtosis38.23521631
Mean11.04196288
Median Absolute Deviation (MAD)0.4137
Skewness-4.758264963
Sum145904533.5
Variance1.134554301
MonotonicityNot monotonic
2022-11-23T16:37:08.637799image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.4457628564
 
4.8%
11.51465620294
 
4.7%
11.37675618965
 
4.7%
11.5836608709
 
4.6%
11.23885586999
 
4.4%
11.7215570816
 
4.3%
11.79045550803
 
4.2%
11.1699549851
 
4.2%
11.8594498123
 
3.8%
11.10095495994
 
3.8%
Other values (208)7484523
56.6%
ValueCountFrequency (%)
0270
 
< 0.1%
0.0689522591
0.2%
0.13793506
 
< 0.1%
0.20685613
 
< 0.1%
0.2758303
 
< 0.1%
0.34475304
 
< 0.1%
0.4137257
 
< 0.1%
0.48265584
 
< 0.1%
0.5516921
 
< 0.1%
0.62055545
 
< 0.1%
ValueCountFrequency (%)
14.9621526277
0.2%
14.893224
 
< 0.1%
14.8242533
 
< 0.1%
14.755331
 
< 0.1%
14.6863541
 
< 0.1%
14.617457
 
< 0.1%
14.5484564
 
< 0.1%
14.479563
 
< 0.1%
14.4105546
 
< 0.1%
14.341632
 
< 0.1%

LongitudAcc
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct138
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.01091301784
Minimum-12.5
Maximum13
Zeros3062406
Zeros (%)23.2%
Negative5504677
Negative (%)41.7%
Memory size100.8 MiB
2022-11-23T16:37:08.789625image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum-12.5
5-th percentile-1
Q1-0.2
median0
Q30.2
95-th percentile0.8
Maximum13
Range25.5
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.7855852151
Coefficient of variation (CV)-71.98606531
Kurtosis156.3525718
Mean-0.01091301784
Median Absolute Deviation (MAD)0.2
Skewness9.368671831
Sum-144200.7
Variance0.6171441302
MonotonicityNot monotonic
2022-11-23T16:37:08.924778image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03062406
23.2%
-0.11277113
9.7%
-0.21109964
 
8.4%
0.11072153
 
8.1%
0.2814254
 
6.2%
-0.3810250
 
6.1%
0.3619201
 
4.7%
-0.4565264
 
4.3%
0.4453130
 
3.4%
0.5365707
 
2.8%
Other values (128)3064199
23.2%
ValueCountFrequency (%)
-12.51
< 0.1%
-9.91
< 0.1%
-8.91
< 0.1%
-82
< 0.1%
-7.61
< 0.1%
-7.31
< 0.1%
-7.21
< 0.1%
-7.11
< 0.1%
-6.82
< 0.1%
-6.72
< 0.1%
ValueCountFrequency (%)
1322204
0.2%
12.95758
 
< 0.1%
7.51
 
< 0.1%
6.51
 
< 0.1%
6.21
 
< 0.1%
6.13
 
< 0.1%
61
 
< 0.1%
5.71
 
< 0.1%
5.61
 
< 0.1%
5.53
 
< 0.1%

EngineSpeed
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct11302
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1073.719842
Minimum0
Maximum8191.875
Zeros226548
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size100.8 MiB
2022-11-23T16:37:09.068759image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile590.75
Q1897.25
median1158.75
Q31288.625
95-th percentile1463.875
Maximum8191.875
Range8191.875
Interquartile range (IQR)391.375

Descriptive statistics

Standard deviation321.1479959
Coefficient of variation (CV)0.2990985015
Kurtosis3.159375967
Mean1073.719842
Median Absolute Deviation (MAD)158.5
Skewness-0.7529860281
Sum1.418774853 × 1010
Variance103136.0353
MonotonicityNot monotonic
2022-11-23T16:37:09.203200image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0226548
 
1.7%
600.2524214
 
0.2%
599.7524160
 
0.2%
60024069
 
0.2%
600.523981
 
0.2%
599.523669
 
0.2%
599.2523294
 
0.2%
59923128
 
0.2%
600.87523062
 
0.2%
601.12522695
 
0.2%
Other values (11292)12774821
96.7%
ValueCountFrequency (%)
0226548
1.7%
13.8751
 
< 0.1%
14.6251
 
< 0.1%
17.6251
 
< 0.1%
18.251
 
< 0.1%
19.1251
 
< 0.1%
19.6251
 
< 0.1%
21.6251
 
< 0.1%
24.3751
 
< 0.1%
24.6251
 
< 0.1%
ValueCountFrequency (%)
8191.875135
< 0.1%
8031.8751
 
< 0.1%
2296.251
 
< 0.1%
22671
 
< 0.1%
2146.8751
 
< 0.1%
2144.51
 
< 0.1%
2141.3751
 
< 0.1%
2138.6251
 
< 0.1%
2137.252
 
< 0.1%
2136.751
 
< 0.1%

Fuel Rate
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct1103
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.65940458
Minimum0
Maximum3876.198645
Zeros3065605
Zeros (%)23.2%
Negative0
Negative (%)0.0%
Memory size100.8 MiB
2022-11-23T16:37:09.350666image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.301234
median8.043992
Q321.88439
95-th percentile48.382246
Maximum3876.198645
Range3876.198645
Interquartile range (IQR)20.583156

Descriptive statistics

Standard deviation79.42546363
Coefficient of variation (CV)5.072061534
Kurtosis2269.129089
Mean15.65940458
Median Absolute Deviation (MAD)8.043992
Skewness46.75265747
Sum206917750.4
Variance6308.404273
MonotonicityNot monotonic
2022-11-23T16:37:09.491901image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03065605
 
23.2%
3.96284990127
 
0.7%
4.02199688371
 
0.7%
3.90370286657
 
0.7%
4.08114385972
 
0.7%
4.1402980986
 
0.6%
3.84455579938
 
0.6%
4.19943776180
 
0.6%
3.78540871163
 
0.5%
4.25858470248
 
0.5%
Other values (1093)9418394
71.3%
ValueCountFrequency (%)
03065605
23.2%
0.05914712406
 
0.1%
0.11829412098
 
0.1%
0.17744114606
 
0.1%
0.23658818361
 
0.1%
0.29573516841
 
0.1%
0.35488215150
 
0.1%
0.41402914139
 
0.1%
0.47317612138
 
0.1%
0.53232310374
 
0.1%
ValueCountFrequency (%)
3876.1986455379
< 0.1%
65.1208471
 
< 0.1%
65.061728
 
< 0.1%
65.00255335
 
< 0.1%
64.94340659
 
< 0.1%
64.88425981
 
< 0.1%
64.82511251
 
< 0.1%
64.76596562
 
< 0.1%
64.70681885
 
< 0.1%
64.64767166
 
< 0.1%

Engine Load
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct201
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.28595491
Minimum0
Maximum100
Zeros3079422
Zeros (%)23.3%
Negative0
Negative (%)0.0%
Memory size100.8 MiB
2022-11-23T16:37:09.662167image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.5
median26
Q346
95-th percentile93.5
Maximum100
Range100
Interquartile range (IQR)42.5

Descriptive statistics

Standard deviation28.20459248
Coefficient of variation (CV)0.901509721
Kurtosis-0.07494185937
Mean31.28595491
Median Absolute Deviation (MAD)21
Skewness0.8332060844
Sum413401376.5
Variance795.4990369
MonotonicityNot monotonic
2022-11-23T16:37:09.804223image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03079422
 
23.3%
100456077
 
3.5%
23.5180986
 
1.4%
23176421
 
1.3%
24172326
 
1.3%
22.5165340
 
1.3%
24.5163258
 
1.2%
25152021
 
1.2%
22147135
 
1.1%
25.5143977
 
1.1%
Other values (191)8376678
63.4%
ValueCountFrequency (%)
03079422
23.3%
0.552303
 
0.4%
140498
 
0.3%
1.531152
 
0.2%
229473
 
0.2%
2.526473
 
0.2%
328233
 
0.2%
3.526454
 
0.2%
429420
 
0.2%
4.527307
 
0.2%
ValueCountFrequency (%)
100456077
3.5%
99.515915
 
0.1%
9915577
 
0.1%
98.518060
 
0.1%
9816468
 
0.1%
97.515875
 
0.1%
9715130
 
0.1%
96.514932
 
0.1%
9615309
 
0.1%
95.514706
 
0.1%

Boost Pressure
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct212
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2487299203
Minimum0
Maximum1.818398
Zeros550952
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size100.8 MiB
2022-11-23T16:37:09.950469image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.008618
Q10.060326
median0.137888
Q30.336102
95-th percentile0.870418
Maximum1.818398
Range1.818398
Interquartile range (IQR)0.275776

Descriptive statistics

Standard deviation0.2841405335
Coefficient of variation (CV)1.142365716
Kurtosis3.765779068
Mean0.2487299203
Median Absolute Deviation (MAD)0.112034
Skewness1.919979745
Sum3286627.873
Variance0.08073584277
MonotonicityNot monotonic
2022-11-23T16:37:10.083828image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0172361126500
 
8.5%
0550952
 
4.2%
0.025854520344
 
3.9%
0.103416503859
 
3.8%
0.094798484109
 
3.7%
0.112034465300
 
3.5%
0.08618399572
 
3.0%
0.120652398964
 
3.0%
0.12927329619
 
2.5%
0.034472316644
 
2.4%
Other values (202)8117778
61.4%
ValueCountFrequency (%)
0550952
4.2%
0.008618220245
 
1.7%
0.0172361126500
8.5%
0.025854520344
3.9%
0.034472316644
 
2.4%
0.04309244221
 
1.8%
0.051708216459
 
1.6%
0.060326211282
 
1.6%
0.068944237335
 
1.8%
0.077562299869
 
2.3%
ValueCountFrequency (%)
1.8183984
< 0.1%
1.809783
 
< 0.1%
1.8011623
 
< 0.1%
1.7925442
 
< 0.1%
1.7839262
 
< 0.1%
1.7753082
 
< 0.1%
1.766691
 
< 0.1%
1.7580725
< 0.1%
1.7494549
< 0.1%
1.7408363
 
< 0.1%

EngineAirInletPressure
Real number (ℝ≥0)

HIGH CORRELATION

Distinct104
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean125.7003492
Minimum34
Maximum510
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size100.8 MiB
2022-11-23T16:37:10.226393image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum34
5-th percentile102
Q1106
median114
Q3134
95-th percentile188
Maximum510
Range476
Interquartile range (IQR)28

Descriptive statistics

Standard deviation28.449274
Coefficient of variation (CV)0.2263261334
Kurtosis4.069696944
Mean125.7003492
Median Absolute Deviation (MAD)10
Skewness1.932728418
Sum1660959288
Variance809.361191
MonotonicityNot monotonic
2022-11-23T16:37:10.357907image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1021280254
 
9.7%
1041037794
 
7.9%
1121036325
 
7.8%
1101010191
 
7.6%
114705172
 
5.3%
108647195
 
4.9%
106579224
 
4.4%
116574572
 
4.3%
118430740
 
3.3%
120374896
 
2.8%
Other values (94)5537278
41.9%
ValueCountFrequency (%)
344
 
< 0.1%
501
 
< 0.1%
524
 
< 0.1%
681
 
< 0.1%
8412
 
< 0.1%
8610
 
< 0.1%
94211
 
< 0.1%
9613110
 
0.1%
9870622
 
0.5%
100350127
2.6%
ValueCountFrequency (%)
510133
< 0.1%
5082
 
< 0.1%
2844
 
< 0.1%
2826
 
< 0.1%
2805
 
< 0.1%
2785
 
< 0.1%
27614
 
< 0.1%
2745
 
< 0.1%
27217
 
< 0.1%
27024
 
< 0.1%

AcceleratorPedalPos
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct251
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.65258012
Minimum0
Maximum100
Zeros5299357
Zeros (%)40.1%
Negative0
Negative (%)0.0%
Memory size100.8 MiB
2022-11-23T16:37:10.494449image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median40.4
Q367.6
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)67.6

Descriptive statistics

Standard deviation35.72304961
Coefficient of variation (CV)0.9487543616
Kurtosis-1.416464228
Mean37.65258012
Median Absolute Deviation (MAD)40.4
Skewness0.2449926402
Sum497527676.4
Variance1276.136274
MonotonicityNot monotonic
2022-11-23T16:37:10.682971image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05299357
40.1%
100809381
 
6.1%
61.661356
 
0.5%
64.861114
 
0.5%
62.461097
 
0.5%
60.861013
 
0.5%
59.660209
 
0.5%
6260110
 
0.5%
6460081
 
0.5%
61.260017
 
0.5%
Other values (241)6619906
50.1%
ValueCountFrequency (%)
05299357
40.1%
0.45214
 
< 0.1%
0.85531
 
< 0.1%
1.25537
 
< 0.1%
1.65688
 
< 0.1%
25493
 
< 0.1%
2.46151
 
< 0.1%
2.85830
 
< 0.1%
3.26063
 
< 0.1%
3.66012
 
< 0.1%
ValueCountFrequency (%)
100809381
6.1%
99.615577
 
0.1%
99.216307
 
0.1%
98.816024
 
0.1%
98.415586
 
0.1%
9816315
 
0.1%
97.617068
 
0.1%
97.216498
 
0.1%
96.816606
 
0.1%
96.417084
 
0.1%

VehicleSpeed
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1055
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.54432759
Minimum0
Maximum255.97971
Zeros1889729
Zeros (%)14.3%
Negative0
Negative (%)0.0%
Memory size100.8 MiB
2022-11-23T16:37:10.850875image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q116.498944
median39.497472
Q357.394764
95-th percentile75.89358
Maximum255.97971
Range255.97971
Interquartile range (IQR)40.89582

Descriptive statistics

Standard deviation24.93111938
Coefficient of variation (CV)0.6640449033
Kurtosis-0.8299271261
Mean37.54432759
Median Absolute Deviation (MAD)20.397132
Skewness0.001613865114
Sum496097266.3
Variance621.5607136
MonotonicityNot monotonic
2022-11-23T16:37:10.992077image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01889729
 
14.3%
48.89530823181
 
0.2%
48.3953422685
 
0.2%
48.09457822680
 
0.2%
45.69629422556
 
0.2%
47.79381622357
 
0.2%
47.09464222166
 
0.2%
47.5946121898
 
0.2%
47.39540421840
 
0.2%
46.09470621807
 
0.2%
Other values (1045)11122742
84.2%
ValueCountFrequency (%)
01889729
14.3%
0.9999365199
 
< 0.1%
1.0975864413
 
< 0.1%
1.1991424848
 
< 0.1%
1.2967926383
 
< 0.1%
1.3983485544
 
< 0.1%
1.4999045700
 
< 0.1%
1.5975549266
 
0.1%
1.699115943
 
< 0.1%
1.796765894
 
< 0.1%
ValueCountFrequency (%)
255.97971135
 
< 0.1%
255.975804491
< 0.1%
115.4926081
 
< 0.1%
115.2894962
 
< 0.1%
115.1918462
 
< 0.1%
114.8910842
 
< 0.1%
114.7895283
 
< 0.1%
114.5903221
 
< 0.1%
114.4926721
 
< 0.1%
114.289561
 
< 0.1%

BrakePedalPos
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct237
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.268935368
Minimum0
Maximum96.8
Zeros10745857
Zeros (%)81.3%
Negative0
Negative (%)0.0%
Memory size100.8 MiB
2022-11-23T16:37:11.135867image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile21.6
Maximum96.8
Range96.8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.493586926
Coefficient of variation (CV)2.292363135
Kurtosis4.201458002
Mean3.268935368
Median Absolute Deviation (MAD)0
Skewness2.211387693
Sum43194538.4
Variance56.15384502
MonotonicityNot monotonic
2022-11-23T16:37:11.287966image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
010745857
81.3%
16118023
 
0.9%
15.2111147
 
0.8%
16.4110151
 
0.8%
16.8102150
 
0.8%
15.6100220
 
0.8%
14.886153
 
0.7%
17.270887
 
0.5%
14.461652
 
0.5%
1858330
 
0.4%
Other values (227)1649071
 
12.5%
ValueCountFrequency (%)
010745857
81.3%
0.444160
 
0.3%
0.816989
 
0.1%
1.212283
 
0.1%
1.611018
 
0.1%
211368
 
0.1%
2.410285
 
0.1%
2.89691
 
0.1%
3.210309
 
0.1%
3.67897
 
0.1%
ValueCountFrequency (%)
96.818
 
< 0.1%
96.449
< 0.1%
961
 
< 0.1%
95.62
 
< 0.1%
95.22
 
< 0.1%
94.83
 
< 0.1%
94.41
 
< 0.1%
93.63
 
< 0.1%
92.82
 
< 0.1%
92.42
 
< 0.1%

Interactions

2022-11-23T16:36:09.955304image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:30:59.826176image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:31:30.017197image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:32:00.688873image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:32:30.978733image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:33:01.689775image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:33:33.212684image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:34:05.171509image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:34:36.556333image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:35:07.587546image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:35:39.332237image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:36:12.789748image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:31:02.571382image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:31:32.665103image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:32:03.436452image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:32:33.770291image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:33:04.375380image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:33:36.171863image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:34:08.040710image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:34:39.355361image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:35:10.433014image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:35:42.080273image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:36:15.679886image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:31:05.346100image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:31:35.413185image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:32:06.094757image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:32:36.581652image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:33:07.048532image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:33:39.111862image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:34:10.924816image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:34:42.166599image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:35:13.277559image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:35:44.840131image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:36:18.529963image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:31:08.064638image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:31:38.115985image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:32:08.867160image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:32:39.296645image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:33:09.757052image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:33:42.026527image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:34:13.784216image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:34:44.992409image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:35:16.354503image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:35:47.606064image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:36:21.314664image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:31:10.777431image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:31:40.843073image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:32:11.560894image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:32:42.052638image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:33:12.377617image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:33:44.894482image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:34:16.641333image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:34:47.774611image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:35:19.390718image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:35:50.319465image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:36:24.218616image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:31:13.560876image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:31:43.637623image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:32:14.359707image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:32:44.908266image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:33:15.084083image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:33:47.725729image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:34:19.541467image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:34:50.644689image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:35:22.298459image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:35:53.129140image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:36:27.029804image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:31:16.329420image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:31:46.399199image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:32:17.144093image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:32:47.712955image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:33:17.808773image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2022-11-23T16:34:53.461001image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:35:25.214037image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2022-11-23T16:31:19.068533image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2022-11-23T16:35:58.897480image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:36:32.672309image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:31:21.851361image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:31:51.927102image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:32:22.708167image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:32:53.388052image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:33:23.950762image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:33:56.516538image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:34:28.070995image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:34:59.047162image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:35:30.880962image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:36:01.683084image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:36:35.415375image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:31:24.567140image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:31:54.717264image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:32:25.472946image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:32:56.191833image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:33:27.245974image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:33:59.445408image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:34:30.942966image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:35:01.892801image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:35:33.741123image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:36:04.379359image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:36:38.035963image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:31:27.278508image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:31:57.932806image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:32:28.183057image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:32:58.998018image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:33:29.991823image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:34:02.326696image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:34:33.786367image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:35:04.758749image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:35:36.590116image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:36:07.135983image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Correlations

2022-11-23T16:37:11.430326image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-23T16:37:11.670300image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-23T16:37:12.220311image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-23T16:37:12.473744image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-23T16:37:12.750648image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-23T16:36:38.518094image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-23T16:36:44.021046image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

TimestampWetTankAirPressureLongitudAccEngineSpeedFuel RateEngine LoadBoost PressureEngineAirInletPressureAcceleratorPedalPosVehicleSpeedBrakePedalPos
01.717422e+1011.583600.21103.75015.49651449.00.120652112.069.219.4987520.0
11.717422e+1011.583600.11194.50021.70694942.00.232686124.063.221.5962740.0
21.717422e+1011.58360-0.61183.0007.21593414.50.189596120.040.821.6978300.0
31.717422e+1011.514650.01156.5003.7262617.50.155124114.030.821.3970680.0
41.717423e+1011.51465-0.81020.2500.0000000.00.112034112.00.019.49875210.4
51.717423e+1011.44570-1.0911.1250.0000000.00.094798110.00.016.59659416.8
61.717423e+1011.44570-0.2615.2500.0000000.00.068944106.00.012.0968820.0
71.717423e+1011.445700.0968.75014.49101540.50.034472104.046.811.7961200.0
81.717423e+1011.376750.51115.12513.78125131.50.051708106.052.412.4992000.0
91.717423e+1011.376750.71247.25012.36172326.00.086180108.055.213.6983420.0

Last rows

TimestampWetTankAirPressureLongitudAccEngineSpeedFuel RateEngine LoadBoost PressureEngineAirInletPressureAcceleratorPedalPosVehicleSpeedBrakePedalPos
132136318.030538e+100.068950.00.00.00.00.0102.00.00.00.0
132136328.030538e+100.068950.00.00.00.00.0102.00.00.00.0
132136338.030538e+100.068950.00.00.00.00.0102.00.00.00.0
132136348.030538e+100.068950.00.00.00.00.0102.00.00.00.0
132136358.030538e+100.068950.00.00.00.00.0102.00.00.00.0
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